{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        activate_func = nn.Sigmoid()\n",
    "        # activate_func = nn.LeakyReLU(0.02)\n",
    "        \n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(784, 100),\n",
    "            activate_func,\n",
    "            nn.Linear(100, 10),\n",
    "            activate_func,\n",
    "        )\n",
    "\n",
    "        self.loss_function = nn.MSELoss()\n",
    "        # self.loss_function = nn.BCELoss()\n",
    "\n",
    "        # self.optimiser = torch.optim.SGD(self.parameters(), lr=0.02)\n",
    "        self.optimiser = torch.optim.Adam(self.parameters())\n",
    "\n",
    "        # record progress\n",
    "        self.counter = 0\n",
    "        self.progress = []\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        return self.model(inputs)\n",
    "\n",
    "    def train(self, inputs, targets):\n",
    "        outputs = self.forward(inputs)\n",
    "        loss = self.loss_function(outputs, targets)\n",
    "\n",
    "        self.optimiser.zero_grad() # set gradient to 0\n",
    "        loss.backward() # backward loss and calculate gradient\n",
    "        self.optimiser.step() # update parameters\n",
    "\n",
    "        # record progress\n",
    "        self.counter += 1\n",
    "        if self.counter % 10 == 0:\n",
    "            self.progress.append(loss.item())\n",
    "        if self.counter % 10000 == 0:\n",
    "            print(f'counter: {self.counter}')\n",
    "\n",
    "    def plot_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=['loss'])\n",
    "        df.plot(ylim=(0, 1.0), figsize=(18, 8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistDataset(Dataset):\n",
    "    def __init__(self, csv_file):\n",
    "        self.data_df = pd.read_csv(csv_file, header=None)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_df)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        label = self.data_df.iloc[index, 0]\n",
    "        target = torch.zeros(10)\n",
    "        target[label] = 1.0\n",
    "        img_values = torch.FloatTensor(self.data_df.iloc[index, 1:].values) / 255.0\n",
    "        return label, img_values, target\n",
    "\n",
    "    def plot_image(self, index):\n",
    "        arr = self.data_df.iloc[index, 1:].values.reshape(28, 28)\n",
    "        plt.title(f'label = {self.data_df.iloc[index, 0]}')\n",
    "        plt.imshow(arr, interpolation='none', cmap='Blues')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_dataset = MnistDataset('mnist_dataset/mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist_dataset.plot_image(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epoch: 0\n",
      "counter: 10000\n",
      "counter: 20000\n",
      "counter: 30000\n",
      "counter: 40000\n",
      "counter: 50000\n",
      "counter: 60000\n",
      "training epoch: 1\n",
      "counter: 70000\n",
      "counter: 80000\n",
      "counter: 90000\n",
      "counter: 100000\n",
      "counter: 110000\n",
      "counter: 120000\n",
      "training epoch: 2\n",
      "counter: 130000\n",
      "counter: 140000\n",
      "counter: 150000\n",
      "counter: 160000\n",
      "counter: 170000\n",
      "counter: 180000\n",
      "Wall time: 6min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "epochs = 3\n",
    "C = Classifier()\n",
    "for i in range(epochs):\n",
    "    print(f'training epoch: {i}')\n",
    "    for label, image_data_tensor, target_tensor in mnist_dataset:\n",
    "        C.train(image_data_tensor, target_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_test_data = MnistDataset('mnist_dataset/mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "record = 43\n",
    "mnist_test_data.plot_image(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD5CAYAAAA3Os7hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOrUlEQVR4nO3cf6zddX3H8eeLFpiA4jLuDPYHsFiC3dwUbyoZW2QRXUHTZm5ZqDE6o/aPyXTRmHXDoGHZotsys2XsRzPQ6KYdmLndxc66Kc7MCbYIAqWg14q21WFFhChOrL73x/myHS/33nNKz/329uPzkTQ93+/3w/m+ub33ec/9nnNuqgpJ0onvpOM9gCRpMgy6JDXCoEtSIwy6JDXCoEtSIwy6JDViZNCTXJ/ka0nuWuB4kvx5ktkkdyS5cPJjSpJGGecR+ruBjYscvwxY1/3ZCvzVsY8lSTpaI4NeVZ8AvrHIks3Ae2rgZuCpSc6e1ICSpPFM4hr6KuDA0PbBbp8kqUcr+zxZkq0MLstw+umnP/eCCy7o8/SSdMK79dZbv15VU/Mdm0TQDwFrhrZXd/sep6q2A9sBpqena8+ePRM4vST96EjypYWOTeKSywzwiu7VLhcBD1XVVydwv5KkozDyEXqS9wOXAGclOQi8FTgZoKr+GtgJXA7MAo8Ar1qqYSVJCxsZ9KraMuJ4Aa+b2ESSpCek1ydF9cScu+1Dx3wf9739xROYRNJy5lv/JakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGjFW0JNsTHJvktkk2+Y5vjbJTUluS3JHkssnP6okaTEjg55kBXAtcBmwHtiSZP2cZW8Bbqiq5wBXAH856UElSYsb5xH6BmC2qvZX1aPADmDznDUFPKW7fSbwlcmNKEkaxzhBXwUcGNo+2O0b9jbg5UkOAjuB35rvjpJsTbInyZ7Dhw8/gXElSQuZ1JOiW4B3V9Vq4HLgvUked99Vtb2qpqtqempqakKnliTBeEE/BKwZ2l7d7Rv2auAGgKr6FPBjwFmTGFCSNJ5xgr4bWJfkvCSnMHjSc2bOmi8DLwBI8kwGQfeaiiT1aGTQq+oIcCWwC9jH4NUse5Nck2RTt+xNwGuTfBZ4P/AbVVVLNbQk6fFWjrOoqnYyeLJzeN/VQ7fvBi6e7GiSpKPhO0UlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaMVbQk2xMcm+S2STbFljz60nuTrI3yfsmO6YkaZSVoxYkWQFcC7wQOAjsTjJTVXcPrVkH/C5wcVU9mOQnl2pgSdL8xnmEvgGYrar9VfUosAPYPGfNa4Frq+pBgKr62mTHlCSNMk7QVwEHhrYPdvuGnQ+cn+STSW5OsnFSA0qSxjPykstR3M864BJgNfCJJM+qqm8OL0qyFdgKsHbt2gmdWpIE4z1CPwSsGdpe3e0bdhCYqarvVdUXgc8xCPwPqartVTVdVdNTU1NPdGZJ0jzGCfpuYF2S85KcAlwBzMxZ808MHp2T5CwGl2D2T25MSdIoI4NeVUeAK4FdwD7ghqram+SaJJu6ZbuAB5LcDdwEvLmqHliqoSVJjzfWNfSq2gnsnLPv6qHbBbyx+yNJOg58p6gkNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNWKsoCfZmOTeJLNJti2y7leTVJLpyY0oSRrHyKAnWQFcC1wGrAe2JFk/z7onA28Abpn0kJKk0cZ5hL4BmK2q/VX1KLAD2DzPut8H3gH8zwTnkySNaZygrwIODG0f7Pb9nyQXAmuq6kOL3VGSrUn2JNlz+PDhox5WkrSwY35SNMlJwJ8Cbxq1tqq2V9V0VU1PTU0d66klSUPGCfohYM3Q9upu32OeDPwM8PEk9wEXATM+MSpJ/Ron6LuBdUnOS3IKcAUw89jBqnqoqs6qqnOr6lzgZmBTVe1ZkoklSfMaGfSqOgJcCewC9gE3VNXeJNck2bTUA0qSxrNynEVVtRPYOWff1QusveTYx5IkHS3fKSpJjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktSIsYKeZGOSe5PMJtk2z/E3Jrk7yR1JPprknMmPKklazMigJ1kBXAtcBqwHtiRZP2fZbcB0Vf0s8AHgjyY9qCRpceM8Qt8AzFbV/qp6FNgBbB5eUFU3VdUj3ebNwOrJjilJGmWcoK8CDgxtH+z2LeTVwL8ey1CSpKO3cpJ3luTlwDTw/AWObwW2Aqxdu3aSp5akH3njPEI/BKwZ2l7d7fshSS4FrgI2VdV357ujqtpeVdNVNT01NfVE5pUkLWCcoO8G1iU5L8kpwBXAzPCCJM8B/oZBzL82+TElSaOMDHpVHQGuBHYB+4AbqmpvkmuSbOqW/TFwBnBjktuTzCxwd5KkJTLWNfSq2gnsnLPv6qHbl054LknSUfKdopLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUiJXHe4Dl7txtHzrm+7jv7S+ewCSStDgfoUtSI8YKepKNSe5NMptk2zzHT03yD93xW5KcO/FJJUmLGhn0JCuAa4HLgPXAliTr5yx7NfBgVT0DeCfwjkkPKkla3DjX0DcAs1W1HyDJDmAzcPfQms3A27rbHwD+IkmqqiY4q44zn0+Qlrdxgr4KODC0fRB43kJrqupIkoeAnwC+PrwoyVZga7f5rST3PpGhh5w19xzHwcgZ0s/PK4vOsRxmWE5z/IjMAMtjjuUwAyyPOSYxwzkLHej1VS5VtR3YPqn7S7KnqqYndX8n6gzLZY7lMMNymWM5zLBc5lgOMyyXOZZ6hnGeFD0ErBnaXt3tm3dNkpXAmcADkxhQkjSecYK+G1iX5LwkpwBXADNz1swAr+xu/xrwMa+fS1K/Rl5y6a6JXwnsAlYA11fV3iTXAHuqaga4DnhvklngGwyi34eJXb45BsthBlgecyyHGWB5zLEcZoDlMcdymAGWxxxLOkN8IC1JbfCdopLUCIMuSY0w6JLUiBPmty0muYDBO1JXdbsOATNVte/4TXX8dB+PVcAtVfWtof0bq+rDPc2wAaiq2t39OoiNwD1VtbOP8y8w03uq6hXH6/zdDL/A4B3Wd1XVR3o65/OAfVX1cJInAduACxm8o/sPq+qhnuZ4PfDBqjowcvHSzfDYq/G+UlX/nuRlwM8D+4DtVfW9Hmf5KeClDF7W/X3gc8D7qurhJTnfifCkaJLfAbYAOxi8UxUGr4e/AthRVW8/XrM9JsmrqupdPZ3r9cDrGHyCPht4Q1X9c3fsM1V1YQ8zvJXB7/dZCfwbg3cP3wS8ENhVVX/QwwxzXz4b4JeAjwFU1aalnqGb49NVtaG7/VoG/zYfBF4E/Esfn59J9gI/170qbTvwCINfw/GCbv9Ll3qGbo6HgG8DXwDeD9xYVYf7OPfQDH/P4PPyNOCbwBnAPzL4WKSqXrnwfz3ROV4PvAT4BHA5cFs3z68Av1lVH5/4Satq2f9h8F3t5Hn2nwJ8/njP183y5R7PdSdwRnf7XGAPg6gD3NbjDCsYfNE8DDyl2/8k4I6eZvgM8HfAJcDzu7+/2t1+fo//HrcN3d4NTHW3Twfu7GmGfcMflznHbu/zY8HgUu6LGLyc+TDwYQbvU3lyTzPc0f29ErgfWNFtp6/Pze58dw6d+zTg493ttUv1dXqiXHL5AfB04Etz9p/dHetFkjsWOgQ8ra85gJOqu8xSVfcluQT4QJJzuln6cKSqvg88kuQL1f0IWVXfSdLXv8k08AbgKuDNVXV7ku9U1X/0dP7HnJTkxxmELNU9Iq2qbyc50tMMdw39lPjZJNNVtSfJ+UBvlxgYXIL7AfAR4CNJTmbwk9wW4E+AqR5mOKm77HI6g5CeyeD9MacCJ/dw/mErGVxqOZXBTwpU1Ze7j8uSnOxE8NvAR5N8nv//RWFrgWcAV/Y4x9OAXwYenLM/wH/1OMf9SZ5dVbcDVNW3krwEuB54Vk8zPJrktKp6BHjuYzuTnElP32S7cLwzyY3d3/dzfD6nzwRuZfB5UEnOrqqvJjmD/r7Bvgb4syRvYfDLnz6V5ACDr5fX9DQDzPn/rcH16hlgJslpPc1wHXAPg58grwJuTLIfuIjBZdu+/C2wO8ktwC/S/VrxJFMMvsFM3AlxDR0gyUkMnmgaflJ0d/cosa8ZrgPeVVX/Oc+x91XVy3qaYzWDR8j/Pc+xi6vqkz3McGpVfXee/WcBZ1fVnUs9wzznfjFwcVX9Xt/nnk8XsKdV1Rd7POdTgPMYfGM7WFX393Xu7vznV9Xn+jznAnM8HaCqvpLkqcClDC6LfrrnOX4aeCaDJ8jvWfLznShBlyQtztehS1IjDLokNcKgS1IjDLokNcKgS1Ij/hfv1Gqj+G5FjwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_data = mnist_test_data[record][1]\n",
    "output = C.forward(image_data)\n",
    "pd.DataFrame(output.detach().numpy()).plot(kind='bar', legend=False, ylim=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9681 10000 0.9681\n"
     ]
    }
   ],
   "source": [
    "score = 0\n",
    "total = 0\n",
    "for label, image_data_tensor, target_tensor in mnist_test_data:\n",
    "    answer = C.forward(image_data_tensor).detach().numpy()\n",
    "    if answer.argmax() == label:\n",
    "        score += 1\n",
    "    total += 1\n",
    "print(score, total, score/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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